Bayesian Analysis for Population Ecology / Edition 1

Bayesian Analysis for Population Ecology / Edition 1

by Ruth King, Byron Morgan, Olivier Gimenez, Steve Brooks
     
 

ISBN-10: 1439811873

ISBN-13: 9781439811870

Pub. Date: 10/30/2009

Publisher: Taylor & Francis

Novel Statistical Tools for Conserving and Managing Populations

By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space

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Overview

Novel Statistical Tools for Conserving and Managing Populations

By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting.

Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book's website.

The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference.

Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.

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Product Details

ISBN-13:
9781439811870
Publisher:
Taylor & Francis
Publication date:
10/30/2009
Series:
Chapman & Hall/CRC Interdisciplinary Statistics Series, #23
Pages:
456
Product dimensions:
6.30(w) x 9.30(h) x 1.20(d)

Table of Contents

INTRODUCTION TO STATISTICAL ANALYSIS OF ECOLOGICAL DATA

Introduction

Population Ecology

Conservation and Management

Data and Models

Bayesian and Classical Statistical Inference

Senescence

Data, Models and Likelihoods

Introduction

Population Data

Modelling Survival

Multi-Site, Multi-State and Movement Data

Covariates and Large Data Sets; Senescence

Combining Information

Modelling Productivity

Parameter Redundancy

Classical Inference Based on the Likelihood

Introduction

Simple Likelihoods

Model Selection

Maximising Log-Likelihoods

Confidence Regions

Computer Packages

BAYESIAN TECHNIQUES AND TOOLS

Bayesian Inference

Introduction

Prior Selection and Elicitation

Prior Sensitivity Analyses

Summarising Posterior Distributions

Directed Acyclic Graphs

Markov Chain Monte Carlo

Monte Carlo Integration

Markov Chains

Markov Chain Monte Carlo (MCMC)

Implementing MCMC

Model Discrimination

Introduction

Bayesian Model Discrimination

Estimating Posterior Model Probabilities

Prior Sensitivity

Model Averaging

Marginal Posterior Distributions

Assessing Temporal/Age Dependence

Improving and Checking Performance

Additional Computational Techniques

MCMC and RJMCMC Computer Programs

R Code (MCMC) for Dipper Data

WinBUGS Code (MCMC) for Dipper Data

MCMC within the Computer Package MARK

R code (RJMCMC) for Model Uncertainty

WinBUGS Code (RJMCMC) for Model Uncertainty

ECOLOGICAL APPLICATIONS

Covariates, Missing Values and Random Effects

Introduction

Covariates

Missing Values

Assessing Covariate Dependence

Random Effects

Prediction

Splines

Multi-State Models

Introduction

Missing Covariate/Auxiliary Variable Approach

Model Discrimination and Averaging

State-Space Modelling

Introduction

Leslie Matrix-Based Models

Non-Leslie-Based Models

Capture-Recapture Data

Closed Populations

Introduction

Models and Notation

Model Fitting

Model Discrimination and Averaging

Line Transects

Appendix A: Common Distributions

Discrete Distributions

Continuous Distributions

Appendix B: Programming in R

Getting Started in R

Useful R Commands

Writing (RJ)MCMC Functions

R Code for Model C/C

R Code for White Stork Covariate Analysis

Appendix C: Programming in WinBUGS

WinBUGS

Calling WinBUGS from R

References

Index

A Summary, Further Reading, and Exercises appear at the end of most chapters.

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